Overview

Dataset statistics

Number of variables29
Number of observations20165
Missing cells0
Missing cells (%)0.0%
Duplicate rows1333
Duplicate rows (%)6.6%
Total size in memory4.5 MiB
Average record size in memory232.0 B

Variable types

Numeric19
Categorical10

Alerts

незачет сразу has constant value ""Constant
Dataset has 1333 (6.6%) duplicate rowsDuplicates
Семестр is highly overall correlated with Накоп зачет сразу and 1 other fieldsHigh correlation
удовлетворительно сразу is highly overall correlated with отлично сразу and 1 other fieldsHigh correlation
отлично сразу is highly overall correlated with удовлетворительно сразу and 1 other fieldsHigh correlation
зачет с исправлением is highly overall correlated with незачет до исправления and 1 other fieldsHigh correlation
незачет до исправления is highly overall correlated with зачет с исправлением and 1 other fieldsHigh correlation
Накоп зачет сразу is highly overall correlated with Семестр and 1 other fieldsHigh correlation
Накоп удовлетворительно сразу is highly overall correlated with удовлетворительно сразуHigh correlation
Накоп хорошо сразу is highly overall correlated with Семестр and 1 other fieldsHigh correlation
Накоп отлично сразу is highly overall correlated with отлично сразуHigh correlation
Накоп зачет с исправлением is highly overall correlated with Накоп незачет до исправления and 1 other fieldsHigh correlation
Накоп удовлетворительно с исправлением is highly overall correlated with Накоп зачет до исправленияHigh correlation
Накоп хорошо с исправлением is highly overall correlated with Накоп удовлетворительно до исправленияHigh correlation
Накоп незачет до исправления is highly overall correlated with Накоп зачет с исправлением and 1 other fieldsHigh correlation
Накоп зачет до исправления is highly overall correlated with Накоп удовлетворительно с исправлениемHigh correlation
Накоп удовлетворительно до исправления is highly overall correlated with Накоп хорошо с исправлениемHigh correlation
удовлетворительно с исправлением is highly overall correlated with зачет с исправлением and 3 other fieldsHigh correlation
хорошо с исправлением is highly overall correlated with удовлетворительно до исправленияHigh correlation
отлично с исправлением is highly overall correlated with хорошо до исправленияHigh correlation
удовлетворительно до исправления is highly overall correlated with хорошо с исправлениемHigh correlation
хорошо до исправления is highly overall correlated with отлично с исправлениемHigh correlation
удовлетворительно с исправлением is highly imbalanced (89.5%)Imbalance
хорошо с исправлением is highly imbalanced (79.4%)Imbalance
отлично с исправлением is highly imbalanced (72.3%)Imbalance
зачет до исправления is highly imbalanced (73.4%)Imbalance
удовлетворительно до исправления is highly imbalanced (81.9%)Imbalance
хорошо до исправления is highly imbalanced (80.2%)Imbalance
зачет с исправлением is highly skewed (γ1 = 34.42537372)Skewed
незачет до исправления is highly skewed (γ1 = 30.9383154)Skewed
Накоп зачет с исправлением is highly skewed (γ1 = 24.6078805)Skewed
Накоп незачет до исправления is highly skewed (γ1 = 20.3191549)Skewed
зачет сразу has 350 (1.7%) zerosZeros
удовлетворительно сразу has 12180 (60.4%) zerosZeros
хорошо сразу has 6912 (34.3%) zerosZeros
отлично сразу has 9337 (46.3%) zerosZeros
зачет с исправлением has 20104 (99.7%) zerosZeros
незачет до исправления has 19992 (99.1%) zerosZeros
Накоп незачет сразу has 19155 (95.0%) zerosZeros
Накоп удовлетворительно сразу has 7927 (39.3%) zerosZeros
Накоп хорошо сразу has 2665 (13.2%) zerosZeros
Накоп отлично сразу has 4411 (21.9%) zerosZeros
Накоп зачет с исправлением has 19876 (98.6%) zerosZeros
Накоп удовлетворительно с исправлением has 18019 (89.4%) zerosZeros
Накоп хорошо с исправлением has 15465 (76.7%) zerosZeros
Накоп отлично с исправлением has 14366 (71.2%) zerosZeros
Накоп незачет до исправления has 19462 (96.5%) zerosZeros
Накоп зачет до исправления has 14717 (73.0%) zerosZeros
Накоп удовлетворительно до исправления has 17032 (84.5%) zerosZeros

Reproduction

Analysis started2023-10-02 12:02:11.330112
Analysis finished2023-10-02 12:03:06.490236
Duration55.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Семестр
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0658071
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:06.640055image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile8
Maximum11
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5276852
Coefficient of variation (CV)0.62169334
Kurtosis-0.78907375
Mean4.0658071
Median Absolute Deviation (MAD)2
Skewness0.54337145
Sum81987
Variance6.3891923
MonotonicityNot monotonic
2023-10-02T19:03:06.730062image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 3637
18.0%
2 3377
16.7%
3 3201
15.9%
4 2079
10.3%
5 1845
9.1%
6 1803
8.9%
7 1619
8.0%
8 1604
8.0%
9 544
 
2.7%
10 443
 
2.2%
ValueCountFrequency (%)
1 3637
18.0%
2 3377
16.7%
3 3201
15.9%
4 2079
10.3%
5 1845
9.1%
6 1803
8.9%
7 1619
8.0%
8 1604
8.0%
9 544
 
2.7%
10 443
 
2.2%
ValueCountFrequency (%)
11 13
 
0.1%
10 443
 
2.2%
9 544
 
2.7%
8 1604
8.0%
7 1619
8.0%
6 1803
8.9%
5 1845
9.1%
4 2079
10.3%
3 3201
15.9%
2 3377
16.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.7 KiB
0
14240 
2
5797 
1
 
128

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20165
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 14240
70.6%
2 5797
28.7%
1 128
 
0.6%

Length

2023-10-02T19:03:06.830405image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T19:03:06.950174image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14240
70.6%
2 5797
28.7%
1 128
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 14240
70.6%
2 5797
28.7%
1 128
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20165
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14240
70.6%
2 5797
28.7%
1 128
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 20165
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14240
70.6%
2 5797
28.7%
1 128
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14240
70.6%
2 5797
28.7%
1 128
 
0.6%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.7 KiB
0
14509 
2
3514 
3
 
1096
1
 
1046

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20165
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14509
72.0%
2 3514
 
17.4%
3 1096
 
5.4%
1 1046
 
5.2%

Length

2023-10-02T19:03:07.050460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T19:03:07.262563image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14509
72.0%
2 3514
 
17.4%
3 1096
 
5.4%
1 1046
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 14509
72.0%
2 3514
 
17.4%
3 1096
 
5.4%
1 1046
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20165
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14509
72.0%
2 3514
 
17.4%
3 1096
 
5.4%
1 1046
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20165
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14509
72.0%
2 3514
 
17.4%
3 1096
 
5.4%
1 1046
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14509
72.0%
2 3514
 
17.4%
3 1096
 
5.4%
1 1046
 
5.2%

незачет сразу
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.7 KiB
0
20165 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20165
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20165
100.0%

Length

2023-10-02T19:03:07.363372image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T19:03:07.461114image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 20165
100.0%

Most occurring characters

ValueCountFrequency (%)
0 20165
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20165
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20165
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20165
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20165
100.0%

зачет сразу
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9500124
Minimum0
Maximum11
Zeros350
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:07.570167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6303937
Coefficient of variation (CV)0.4127566
Kurtosis0.27167512
Mean3.9500124
Median Absolute Deviation (MAD)1
Skewness-0.038516143
Sum79652
Variance2.6581835
MonotonicityNot monotonic
2023-10-02T19:03:07.674002image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 5753
28.5%
5 4227
21.0%
3 2948
14.6%
6 2473
12.3%
2 2426
12.0%
1 1226
 
6.1%
7 526
 
2.6%
0 350
 
1.7%
8 131
 
0.6%
10 50
 
0.2%
Other values (2) 55
 
0.3%
ValueCountFrequency (%)
0 350
 
1.7%
1 1226
 
6.1%
2 2426
12.0%
3 2948
14.6%
4 5753
28.5%
5 4227
21.0%
6 2473
12.3%
7 526
 
2.6%
8 131
 
0.6%
9 45
 
0.2%
ValueCountFrequency (%)
11 10
 
< 0.1%
10 50
 
0.2%
9 45
 
0.2%
8 131
 
0.6%
7 526
 
2.6%
6 2473
12.3%
5 4227
21.0%
4 5753
28.5%
3 2948
14.6%
2 2426
12.0%

удовлетворительно сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76047607
Minimum0
Maximum8
Zeros12180
Zeros (%)60.4%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:07.760110image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1351601
Coefficient of variation (CV)1.4926967
Kurtosis1.5165495
Mean0.76047607
Median Absolute Deviation (MAD)0
Skewness1.4810711
Sum15335
Variance1.2885885
MonotonicityNot monotonic
2023-10-02T19:03:07.850426image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 12180
60.4%
1 3496
 
17.3%
2 2425
 
12.0%
3 1388
 
6.9%
4 577
 
2.9%
5 80
 
0.4%
6 17
 
0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 12180
60.4%
1 3496
 
17.3%
2 2425
 
12.0%
3 1388
 
6.9%
4 577
 
2.9%
5 80
 
0.4%
6 17
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 17
 
0.1%
5 80
 
0.4%
4 577
 
2.9%
3 1388
 
6.9%
2 2425
 
12.0%
1 3496
 
17.3%
0 12180
60.4%

хорошо сразу
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2533598
Minimum0
Maximum11
Zeros6912
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:07.955181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2090256
Coefficient of variation (CV)0.96462775
Kurtosis1.0378137
Mean1.2533598
Median Absolute Deviation (MAD)1
Skewness0.88151183
Sum25274
Variance1.461743
MonotonicityNot monotonic
2023-10-02T19:03:08.040288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 6912
34.3%
1 5639
28.0%
2 4341
21.5%
3 2336
 
11.6%
4 821
 
4.1%
5 81
 
0.4%
6 18
 
0.1%
9 6
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
0 6912
34.3%
1 5639
28.0%
2 4341
21.5%
3 2336
 
11.6%
4 821
 
4.1%
5 81
 
0.4%
6 18
 
0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 2
 
< 0.1%
9 6
 
< 0.1%
8 3
 
< 0.1%
7 4
 
< 0.1%
6 18
 
0.1%
5 81
 
0.4%
4 821
 
4.1%
3 2336
11.6%
2 4341
21.5%

отлично сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1183238
Minimum0
Maximum13
Zeros9337
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:08.140338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3383558
Coefficient of variation (CV)1.1967515
Kurtosis2.2416734
Mean1.1183238
Median Absolute Deviation (MAD)1
Skewness1.2371923
Sum22551
Variance1.7911962
MonotonicityNot monotonic
2023-10-02T19:03:08.250758image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 9337
46.3%
1 4410
21.9%
2 2799
 
13.9%
3 2228
 
11.0%
4 1231
 
6.1%
5 113
 
0.6%
6 20
 
0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
Other values (4) 10
 
< 0.1%
ValueCountFrequency (%)
0 9337
46.3%
1 4410
21.9%
2 2799
 
13.9%
3 2228
 
11.0%
4 1231
 
6.1%
5 113
 
0.6%
6 20
 
0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
13 3
 
< 0.1%
12 3
 
< 0.1%
11 3
 
< 0.1%
10 1
 
< 0.1%
9 4
 
< 0.1%
8 5
 
< 0.1%
7 8
 
< 0.1%
6 20
 
0.1%
5 113
 
0.6%
4 1231
6.1%

зачет с исправлением
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004363997
Minimum0
Maximum5
Zeros20104
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:08.370166image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.098997146
Coefficient of variation (CV)22.684971
Kurtosis1456.7149
Mean0.004363997
Median Absolute Deviation (MAD)0
Skewness34.425374
Sum88
Variance0.0098004348
MonotonicityNot monotonic
2023-10-02T19:03:08.450219image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 20104
99.7%
1 50
 
0.2%
2 4
 
< 0.1%
5 3
 
< 0.1%
4 3
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 20104
99.7%
1 50
 
0.2%
2 4
 
< 0.1%
3 1
 
< 0.1%
4 3
 
< 0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
5 3
 
< 0.1%
4 3
 
< 0.1%
3 1
 
< 0.1%
2 4
 
< 0.1%
1 50
 
0.2%
0 20104
99.7%

удовлетворительно с исправлением
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.7 KiB
0.0
19531 
1.0
 
603
2.0
 
30
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60495
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19531
96.9%
1.0 603
 
3.0%
2.0 30
 
0.1%
3.0 1
 
< 0.1%

Length

2023-10-02T19:03:08.550383image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T19:03:08.660223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19531
96.9%
1.0 603
 
3.0%
2.0 30
 
0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 39696
65.6%
. 20165
33.3%
1 603
 
1.0%
2 30
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40330
66.7%
Other Punctuation 20165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39696
98.4%
1 603
 
1.5%
2 30
 
0.1%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39696
65.6%
. 20165
33.3%
1 603
 
1.0%
2 30
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39696
65.6%
. 20165
33.3%
1 603
 
1.0%
2 30
 
< 0.1%
3 1
 
< 0.1%

хорошо с исправлением
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.7 KiB
0.0
18310 
1.0
 
1725
2.0
 
125
3.0
 
3
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60495
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 18310
90.8%
1.0 1725
 
8.6%
2.0 125
 
0.6%
3.0 3
 
< 0.1%
4.0 2
 
< 0.1%

Length

2023-10-02T19:03:08.767805image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T19:03:08.880324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18310
90.8%
1.0 1725
 
8.6%
2.0 125
 
0.6%
3.0 3
 
< 0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 38475
63.6%
. 20165
33.3%
1 1725
 
2.9%
2 125
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40330
66.7%
Other Punctuation 20165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38475
95.4%
1 1725
 
4.3%
2 125
 
0.3%
3 3
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38475
63.6%
. 20165
33.3%
1 1725
 
2.9%
2 125
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38475
63.6%
. 20165
33.3%
1 1725
 
2.9%
2 125
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

отлично с исправлением
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.7 KiB
0.0
17468 
1.0
2371 
2.0
 
310
3.0
 
14
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60495
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17468
86.6%
1.0 2371
 
11.8%
2.0 310
 
1.5%
3.0 14
 
0.1%
4.0 2
 
< 0.1%

Length

2023-10-02T19:03:08.980541image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T19:03:09.095111image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17468
86.6%
1.0 2371
 
11.8%
2.0 310
 
1.5%
3.0 14
 
0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 37633
62.2%
. 20165
33.3%
1 2371
 
3.9%
2 310
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40330
66.7%
Other Punctuation 20165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37633
93.3%
1 2371
 
5.9%
2 310
 
0.8%
3 14
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37633
62.2%
. 20165
33.3%
1 2371
 
3.9%
2 310
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37633
62.2%
. 20165
33.3%
1 2371
 
3.9%
2 310
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

незачет до исправления
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01190181
Minimum0
Maximum8
Zeros19992
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:09.190427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18134911
Coefficient of variation (CV)15.237104
Kurtosis1234.6623
Mean0.01190181
Median Absolute Deviation (MAD)0
Skewness30.938315
Sum240
Variance0.032887501
MonotonicityNot monotonic
2023-10-02T19:03:09.276098image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 19992
99.1%
1 152
 
0.8%
2 11
 
0.1%
8 6
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 19992
99.1%
1 152
 
0.8%
2 11
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 6
 
< 0.1%
ValueCountFrequency (%)
8 6
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 11
 
0.1%
1 152
 
0.8%
0 19992
99.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.7 KiB
0.0
18077 
1.0
1868 
2.0
 
218
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60495
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18077
89.6%
1.0 1868
 
9.3%
2.0 218
 
1.1%
3.0 2
 
< 0.1%

Length

2023-10-02T19:03:09.380341image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T19:03:09.490229image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18077
89.6%
1.0 1868
 
9.3%
2.0 218
 
1.1%
3.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 38242
63.2%
. 20165
33.3%
1 1868
 
3.1%
2 218
 
0.4%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40330
66.7%
Other Punctuation 20165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38242
94.8%
1 1868
 
4.6%
2 218
 
0.5%
3 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38242
63.2%
. 20165
33.3%
1 1868
 
3.1%
2 218
 
0.4%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38242
63.2%
. 20165
33.3%
1 1868
 
3.1%
2 218
 
0.4%
3 2
 
< 0.1%

удовлетворительно до исправления
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.7 KiB
0.0
18900 
1.0
 
1172
2.0
 
92
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60495
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 18900
93.7%
1.0 1172
 
5.8%
2.0 92
 
0.5%
3.0 1
 
< 0.1%

Length

2023-10-02T19:03:09.590757image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T19:03:09.690427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18900
93.7%
1.0 1172
 
5.8%
2.0 92
 
0.5%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 39065
64.6%
. 20165
33.3%
1 1172
 
1.9%
2 92
 
0.2%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40330
66.7%
Other Punctuation 20165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39065
96.9%
1 1172
 
2.9%
2 92
 
0.2%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39065
64.6%
. 20165
33.3%
1 1172
 
1.9%
2 92
 
0.2%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39065
64.6%
. 20165
33.3%
1 1172
 
1.9%
2 92
 
0.2%
3 1
 
< 0.1%

хорошо до исправления
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.7 KiB
0.0
18460 
1.0
 
1541
2.0
 
157
3.0
 
5
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60495
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18460
91.5%
1.0 1541
 
7.6%
2.0 157
 
0.8%
3.0 5
 
< 0.1%
4.0 2
 
< 0.1%

Length

2023-10-02T19:03:09.790007image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T19:03:09.900023image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18460
91.5%
1.0 1541
 
7.6%
2.0 157
 
0.8%
3.0 5
 
< 0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 38625
63.8%
. 20165
33.3%
1 1541
 
2.5%
2 157
 
0.3%
3 5
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40330
66.7%
Other Punctuation 20165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38625
95.8%
1 1541
 
3.8%
2 157
 
0.4%
3 5
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38625
63.8%
. 20165
33.3%
1 1541
 
2.5%
2 157
 
0.3%
3 5
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38625
63.8%
. 20165
33.3%
1 1541
 
2.5%
2 157
 
0.3%
3 5
 
< 0.1%
4 2
 
< 0.1%

Накоп незачет сразу
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30300025
Minimum0
Maximum39
Zeros19155
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:10.000296image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum39
Range39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7453522
Coefficient of variation (CV)5.7602334
Kurtosis92.146763
Mean0.30300025
Median Absolute Deviation (MAD)0
Skewness8.4460722
Sum6110
Variance3.0462541
MonotonicityNot monotonic
2023-10-02T19:03:10.110440image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 19155
95.0%
2 314
 
1.6%
3 120
 
0.6%
4 106
 
0.5%
5 71
 
0.4%
6 67
 
0.3%
8 59
 
0.3%
7 46
 
0.2%
10 31
 
0.2%
11 30
 
0.1%
Other values (17) 166
 
0.8%
ValueCountFrequency (%)
0 19155
95.0%
2 314
 
1.6%
3 120
 
0.6%
4 106
 
0.5%
5 71
 
0.4%
6 67
 
0.3%
7 46
 
0.2%
8 59
 
0.3%
9 25
 
0.1%
10 31
 
0.2%
ValueCountFrequency (%)
39 1
 
< 0.1%
35 1
 
< 0.1%
31 1
 
< 0.1%
28 3
 
< 0.1%
26 3
 
< 0.1%
24 3
 
< 0.1%
22 9
< 0.1%
21 9
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%

Накоп зачет сразу
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.96727
Minimum0
Maximum72
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:10.230290image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median15
Q328
95-th percentile39
Maximum72
Range72
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.208974
Coefficient of variation (CV)0.67951189
Kurtosis-0.45974596
Mean17.96727
Median Absolute Deviation (MAD)9
Skewness0.66165568
Sum362310
Variance149.05904
MonotonicityNot monotonic
2023-10-02T19:03:10.355108image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1913
 
9.5%
8 1242
 
6.2%
10 1146
 
5.7%
11 1122
 
5.6%
9 815
 
4.0%
5 759
 
3.8%
15 659
 
3.3%
29 536
 
2.7%
13 507
 
2.5%
20 470
 
2.3%
Other values (61) 10996
54.5%
ValueCountFrequency (%)
0 14
 
0.1%
1 52
 
0.3%
2 306
 
1.5%
3 458
 
2.3%
4 1913
9.5%
5 759
 
3.8%
6 462
 
2.3%
7 424
 
2.1%
8 1242
6.2%
9 815
4.0%
ValueCountFrequency (%)
72 1
 
< 0.1%
70 1
 
< 0.1%
69 2
 
< 0.1%
67 3
< 0.1%
66 3
< 0.1%
65 2
 
< 0.1%
64 4
< 0.1%
63 2
 
< 0.1%
62 5
< 0.1%
61 4
< 0.1%

Накоп удовлетворительно сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1760476
Minimum0
Maximum64
Zeros7927
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:10.480191image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile17
Maximum64
Range64
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.208828
Coefficient of variation (CV)1.4867714
Kurtosis9.0698505
Mean4.1760476
Median Absolute Deviation (MAD)1
Skewness2.4686864
Sum84210
Variance38.549545
MonotonicityNot monotonic
2023-10-02T19:03:10.600088image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7927
39.3%
1 2169
 
10.8%
2 1653
 
8.2%
3 1152
 
5.7%
4 985
 
4.9%
5 862
 
4.3%
6 673
 
3.3%
7 596
 
3.0%
8 544
 
2.7%
10 478
 
2.4%
Other values (49) 3126
 
15.5%
ValueCountFrequency (%)
0 7927
39.3%
1 2169
 
10.8%
2 1653
 
8.2%
3 1152
 
5.7%
4 985
 
4.9%
5 862
 
4.3%
6 673
 
3.3%
7 596
 
3.0%
8 544
 
2.7%
9 433
 
2.1%
ValueCountFrequency (%)
64 1
< 0.1%
62 1
< 0.1%
61 2
< 0.1%
59 1
< 0.1%
58 1
< 0.1%
56 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%
52 1
< 0.1%
50 2
< 0.1%

Накоп хорошо сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0155219
Minimum0
Maximum38
Zeros2665
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:10.725459image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile17
Maximum38
Range38
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.4158828
Coefficient of variation (CV)0.90031802
Kurtosis0.57534233
Mean6.0155219
Median Absolute Deviation (MAD)4
Skewness0.99594942
Sum121303
Variance29.331786
MonotonicityNot monotonic
2023-10-02T19:03:10.830089image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 2665
13.2%
1 2190
10.9%
2 2077
10.3%
3 1775
 
8.8%
4 1354
 
6.7%
5 1185
 
5.9%
6 1132
 
5.6%
7 977
 
4.8%
8 915
 
4.5%
9 913
 
4.5%
Other values (26) 4982
24.7%
ValueCountFrequency (%)
0 2665
13.2%
1 2190
10.9%
2 2077
10.3%
3 1775
8.8%
4 1354
6.7%
5 1185
5.9%
6 1132
5.6%
7 977
 
4.8%
8 915
 
4.5%
9 913
 
4.5%
ValueCountFrequency (%)
38 1
 
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%
32 2
 
< 0.1%
31 4
< 0.1%
30 3
 
< 0.1%
29 5
< 0.1%
28 7
< 0.1%
27 9
< 0.1%
26 8
< 0.1%

Накоп отлично сразу
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2745847
Minimum0
Maximum42
Zeros4411
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:10.950303image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q36
95-th percentile16
Maximum42
Range42
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.3081433
Coefficient of variation (CV)1.2417916
Kurtosis5.0201892
Mean4.2745847
Median Absolute Deviation (MAD)2
Skewness2.0896664
Sum86197
Variance28.176385
MonotonicityNot monotonic
2023-10-02T19:03:11.060358image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 4411
21.9%
1 3170
15.7%
2 2767
13.7%
3 2079
10.3%
4 1318
 
6.5%
5 1009
 
5.0%
6 985
 
4.9%
7 684
 
3.4%
8 622
 
3.1%
9 523
 
2.6%
Other values (30) 2597
12.9%
ValueCountFrequency (%)
0 4411
21.9%
1 3170
15.7%
2 2767
13.7%
3 2079
10.3%
4 1318
 
6.5%
5 1009
 
5.0%
6 985
 
4.9%
7 684
 
3.4%
8 622
 
3.1%
9 523
 
2.6%
ValueCountFrequency (%)
42 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 4
 
< 0.1%
32 6
 
< 0.1%
31 8
< 0.1%
30 19
0.1%

Накоп зачет с исправлением
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020034714
Minimum0
Maximum12
Zeros19876
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:11.160233image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.22445972
Coefficient of variation (CV)11.20354
Kurtosis915.76948
Mean0.020034714
Median Absolute Deviation (MAD)0
Skewness24.60788
Sum404
Variance0.050382165
MonotonicityNot monotonic
2023-10-02T19:03:11.350413image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 19876
98.6%
1 234
 
1.2%
2 38
 
0.2%
3 6
 
< 0.1%
5 5
 
< 0.1%
9 3
 
< 0.1%
6 2
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
0 19876
98.6%
1 234
 
1.2%
2 38
 
0.2%
3 6
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
9 3
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
9 3
 
< 0.1%
6 2
 
< 0.1%
5 5
 
< 0.1%
3 6
 
< 0.1%
2 38
 
0.2%
1 234
 
1.2%
0 19876
98.6%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15140094
Minimum0
Maximum7
Zeros18019
Zeros (%)89.4%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:11.440521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51233413
Coefficient of variation (CV)3.383956
Kurtosis28.868843
Mean0.15140094
Median Absolute Deviation (MAD)0
Skewness4.6370987
Sum3053
Variance0.26248626
MonotonicityNot monotonic
2023-10-02T19:03:11.525014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 18019
89.4%
1 1550
 
7.7%
2 371
 
1.8%
3 168
 
0.8%
4 40
 
0.2%
5 11
 
0.1%
7 6
 
< 0.1%
ValueCountFrequency (%)
0 18019
89.4%
1 1550
 
7.7%
2 371
 
1.8%
3 168
 
0.8%
4 40
 
0.2%
5 11
 
0.1%
7 6
 
< 0.1%
ValueCountFrequency (%)
7 6
 
< 0.1%
5 11
 
0.1%
4 40
 
0.2%
3 168
 
0.8%
2 371
 
1.8%
1 1550
 
7.7%
0 18019
89.4%

Накоп хорошо с исправлением
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36191421
Minimum0
Maximum7
Zeros15465
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:11.620076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78389962
Coefficient of variation (CV)2.1659819
Kurtosis8.8926605
Mean0.36191421
Median Absolute Deviation (MAD)0
Skewness2.7391415
Sum7298
Variance0.61449862
MonotonicityNot monotonic
2023-10-02T19:03:11.710105image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 15465
76.7%
1 2995
 
14.9%
2 1099
 
5.5%
3 391
 
1.9%
4 159
 
0.8%
5 41
 
0.2%
6 14
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 15465
76.7%
1 2995
 
14.9%
2 1099
 
5.5%
3 391
 
1.9%
4 159
 
0.8%
5 41
 
0.2%
6 14
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 14
 
0.1%
5 41
 
0.2%
4 159
 
0.8%
3 391
 
1.9%
2 1099
 
5.5%
1 2995
 
14.9%
0 15465
76.7%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46853459
Minimum0
Maximum8
Zeros14366
Zeros (%)71.2%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:11.800419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91341273
Coefficient of variation (CV)1.9495097
Kurtosis8.5749481
Mean0.46853459
Median Absolute Deviation (MAD)0
Skewness2.6057177
Sum9448
Variance0.83432281
MonotonicityNot monotonic
2023-10-02T19:03:11.890308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 14366
71.2%
1 3580
 
17.8%
2 1322
 
6.6%
3 561
 
2.8%
4 206
 
1.0%
5 84
 
0.4%
6 29
 
0.1%
7 13
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
0 14366
71.2%
1 3580
 
17.8%
2 1322
 
6.6%
3 561
 
2.8%
4 206
 
1.0%
5 84
 
0.4%
6 29
 
0.1%
7 13
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
8 4
 
< 0.1%
7 13
 
0.1%
6 29
 
0.1%
5 84
 
0.4%
4 206
 
1.0%
3 561
 
2.8%
2 1322
 
6.6%
1 3580
 
17.8%
0 14366
71.2%

Накоп незачет до исправления
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057376643
Minimum0
Maximum22
Zeros19462
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:12.000123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45555353
Coefficient of variation (CV)7.9397036
Kurtosis645.77877
Mean0.057376643
Median Absolute Deviation (MAD)0
Skewness20.319155
Sum1157
Variance0.20752902
MonotonicityNot monotonic
2023-10-02T19:03:12.085063image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 19462
96.5%
1 513
 
2.5%
2 114
 
0.6%
3 24
 
0.1%
5 20
 
0.1%
4 13
 
0.1%
6 8
 
< 0.1%
8 3
 
< 0.1%
16 3
 
< 0.1%
12 2
 
< 0.1%
Other values (2) 3
 
< 0.1%
ValueCountFrequency (%)
0 19462
96.5%
1 513
 
2.5%
2 114
 
0.6%
3 24
 
0.1%
4 13
 
0.1%
5 20
 
0.1%
6 8
 
< 0.1%
8 3
 
< 0.1%
12 2
 
< 0.1%
13 2
 
< 0.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
16 3
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
8 3
 
< 0.1%
6 8
 
< 0.1%
5 20
 
0.1%
4 13
 
0.1%
3 24
 
0.1%
2 114
0.6%

Накоп зачет до исправления
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41105877
Minimum0
Maximum7
Zeros14717
Zeros (%)73.0%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:12.170435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81573903
Coefficient of variation (CV)1.9844828
Kurtosis7.4882876
Mean0.41105877
Median Absolute Deviation (MAD)0
Skewness2.5198526
Sum8289
Variance0.66543017
MonotonicityNot monotonic
2023-10-02T19:03:12.250262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 14717
73.0%
1 3631
 
18.0%
2 1105
 
5.5%
3 477
 
2.4%
4 169
 
0.8%
5 58
 
0.3%
6 5
 
< 0.1%
7 3
 
< 0.1%
ValueCountFrequency (%)
0 14717
73.0%
1 3631
 
18.0%
2 1105
 
5.5%
3 477
 
2.4%
4 169
 
0.8%
5 58
 
0.3%
6 5
 
< 0.1%
7 3
 
< 0.1%
ValueCountFrequency (%)
7 3
 
< 0.1%
6 5
 
< 0.1%
5 58
 
0.3%
4 169
 
0.8%
3 477
 
2.4%
2 1105
 
5.5%
1 3631
 
18.0%
0 14717
73.0%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24815274
Minimum0
Maximum6
Zeros17032
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size157.7 KiB
2023-10-02T19:03:12.350439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70042084
Coefficient of variation (CV)2.8225392
Kurtosis18.169881
Mean0.24815274
Median Absolute Deviation (MAD)0
Skewness3.840259
Sum5004
Variance0.49058935
MonotonicityNot monotonic
2023-10-02T19:03:12.430032image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 17032
84.5%
1 2005
 
9.9%
2 696
 
3.5%
3 232
 
1.2%
4 118
 
0.6%
5 53
 
0.3%
6 29
 
0.1%
ValueCountFrequency (%)
0 17032
84.5%
1 2005
 
9.9%
2 696
 
3.5%
3 232
 
1.2%
4 118
 
0.6%
5 53
 
0.3%
6 29
 
0.1%
ValueCountFrequency (%)
6 29
 
0.1%
5 53
 
0.3%
4 118
 
0.6%
3 232
 
1.2%
2 696
 
3.5%
1 2005
 
9.9%
0 17032
84.5%

отчислен
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.7 KiB
0
16824 
1
3341 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20165
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16824
83.4%
1 3341
 
16.6%

Length

2023-10-02T19:03:12.540414image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-02T19:03:12.650485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 16824
83.4%
1 3341
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 16824
83.4%
1 3341
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20165
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16824
83.4%
1 3341
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 20165
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16824
83.4%
1 3341
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16824
83.4%
1 3341
 
16.6%

Interactions

2023-10-02T19:03:03.296175image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:13.747759image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:16.180085image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:18.941526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:22.130411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:25.090207image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:27.840150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:30.954470image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:35.630160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:38.770608image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:41.438481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:43.920448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:46.230047image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:48.456197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:50.873025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:53.248421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:55.799924image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:58.622866image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:03:00.880544image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:03:03.410073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:13.850280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:16.305246image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:19.070389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:22.315350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:25.219983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:27.971882image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:31.110274image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:35.790323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:38.891620image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:41.550451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:44.033161image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:46.330577image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:48.560124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:50.982537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:53.350503image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:55.967898image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:58.731621image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:03:00.994201image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:03:03.520467image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:13.960555image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:16.440530image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:19.200304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:22.442546image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:25.350060image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:28.092775image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:31.370145image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:36.007197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:39.046628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-02T19:02:21.045410image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:24.234352image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:26.970176image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:02:29.850757image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-02T19:02:53.123056image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-02T19:02:58.490443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:03:00.761101image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-02T19:03:03.180070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-10-02T19:03:12.750428image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Семестрзачет сразуудовлетворительно сразухорошо сразуотлично сразузачет с исправлениемнезачет до исправленияНакоп незачет сразуНакоп зачет сразуНакоп удовлетворительно сразуНакоп хорошо сразуНакоп отлично сразуНакоп зачет с исправлениемНакоп удовлетворительно с исправлениемНакоп хорошо с исправлениемНакоп отлично с исправлениемНакоп незачет до исправленияНакоп зачет до исправленияНакоп удовлетворительно до исправленияФорма обученияКвалификацияудовлетворительно с исправлениемхорошо с исправлениемотлично с исправлениемзачет до исправленияудовлетворительно до исправленияхорошо до исправленияотчислен
Семестр1.000-0.0650.019-0.035-0.043-0.030-0.0350.1750.9270.4630.7060.4520.0540.3020.4830.4150.0930.4960.4080.2390.2700.0840.1060.1000.1610.1180.0800.110
зачет сразу-0.0651.000-0.0020.1960.148-0.044-0.037-0.0990.171-0.0380.1160.076-0.081-0.086-0.0370.011-0.0880.005-0.0480.2430.3100.0710.0500.0750.1210.0380.0640.116
удовлетворительно сразу0.019-0.0021.000-0.100-0.510-0.007-0.0040.1020.0440.6690.019-0.4660.0440.1840.086-0.2040.047-0.0100.1490.2590.2010.0760.0550.0840.0170.0800.0650.250
хорошо сразу-0.0350.196-0.1001.000-0.285-0.020-0.030-0.0280.0310.0520.440-0.182-0.034-0.0060.039-0.037-0.0400.001-0.0130.0640.1310.0000.0140.0450.0400.0510.0330.017
отлично сразу-0.0430.148-0.510-0.2851.0000.008-0.011-0.1200.008-0.494-0.1500.670-0.038-0.183-0.1650.137-0.0580.020-0.2150.2340.1940.0460.0590.0480.1020.0870.0470.207
зачет с исправлением-0.030-0.044-0.007-0.0200.0081.0000.5930.020-0.042-0.018-0.034-0.0070.4570.0460.0140.0040.293-0.015-0.0110.0160.0500.5950.4110.0000.0000.0000.0000.044
незачет до исправления-0.035-0.037-0.004-0.030-0.0110.5931.0000.053-0.043-0.008-0.044-0.0220.2970.1040.0270.0180.490-0.026-0.0110.0200.0540.6110.4090.0240.0000.0000.0000.062
Накоп незачет сразу0.175-0.0990.102-0.028-0.1200.0200.0531.0000.1740.2440.132-0.0400.1810.1770.1280.0100.2280.0970.1110.1060.0450.0310.0210.0000.0160.0210.0000.384
Накоп зачет сразу0.9270.1710.0440.0310.008-0.042-0.0430.1741.0000.4770.7500.4640.0370.2680.4550.4160.0800.4830.3820.1450.3110.0770.1020.0830.1610.1100.0620.172
Накоп удовлетворительно сразу0.463-0.0380.6690.052-0.494-0.018-0.0080.2440.4771.0000.415-0.3030.0810.3760.368-0.0110.1060.2430.3760.2610.1750.0950.0710.0340.0520.0850.0240.211
Накоп хорошо сразу0.7060.1160.0190.440-0.150-0.034-0.0440.1320.7500.4151.0000.2360.0210.2430.4300.3040.0520.3950.3220.1190.2850.0540.0910.0520.1040.0810.0430.091
Накоп отлично сразу0.4520.076-0.466-0.1820.670-0.007-0.022-0.0400.464-0.3030.2361.000-0.016-0.0300.0970.406-0.0100.297-0.0030.1400.1450.0250.0250.0860.0760.0340.0450.181
Накоп зачет с исправлением0.054-0.0810.044-0.034-0.0380.4570.2970.1810.0370.0810.021-0.0161.0000.1450.0860.0020.6400.0360.0610.0240.0380.5870.4090.0000.0040.0000.0000.078
Накоп удовлетворительно с исправлением0.302-0.0860.184-0.006-0.1830.0460.1040.1770.2680.3760.243-0.0300.1451.0000.2080.0100.2740.5180.2110.1470.1020.4060.0670.0090.1720.0710.0000.107
Накоп хорошо с исправлением0.483-0.0370.0860.039-0.1650.0140.0270.1280.4550.3680.4300.0970.0860.2081.0000.2230.1500.4820.7350.1240.1320.0270.3410.0370.1360.2790.0270.026
Накоп отлично с исправлением0.4150.011-0.204-0.0370.1370.0040.0180.0100.416-0.0110.3040.4060.0020.0100.2231.0000.0730.3860.2250.0680.0950.0260.0250.3720.1270.0660.2980.139
Накоп незачет до исправления0.093-0.0880.047-0.040-0.0580.2930.4900.2280.0800.1060.052-0.0100.6400.2740.1500.0731.0000.0200.1200.0280.0460.5940.4130.0000.0000.0000.0000.090
Накоп зачет до исправления0.4960.005-0.0100.0010.020-0.015-0.0260.0970.4830.2430.3950.2970.0360.5180.4820.3860.0201.0000.1710.0910.1700.1780.1570.1340.3850.0600.0230.085
Накоп удовлетворительно до исправления0.408-0.0480.149-0.013-0.215-0.011-0.0110.1110.3820.3760.322-0.0030.0610.2110.7350.2250.1200.1711.0000.1640.1120.0150.2260.0360.0210.3750.0130.026
Форма обучения0.2390.2430.2590.0640.2340.0160.0200.1060.1450.2610.1190.1400.0240.1470.1240.0680.0280.0910.1641.0000.2840.0680.0450.0770.0210.0770.0650.200
Квалификация0.2700.3100.2010.1310.1940.0500.0540.0450.3110.1750.2850.1450.0380.1020.1320.0950.0460.1700.1120.2841.0000.0490.0650.0950.1000.0550.0790.176
удовлетворительно с исправлением0.0840.0710.0760.0000.0460.5950.6110.0310.0770.0950.0540.0250.5870.4060.0270.0260.5940.1780.0150.0680.0491.0000.0000.0280.3240.0000.0230.043
хорошо с исправлением0.1060.0500.0550.0140.0590.4110.4090.0210.1020.0710.0910.0250.4090.0670.3410.0250.4130.1570.2260.0450.0650.0001.0000.0160.3210.6360.0100.024
отлично с исправлением0.1000.0750.0840.0450.0480.0000.0240.0000.0830.0340.0520.0860.0000.0090.0370.3720.0000.1340.0360.0770.0950.0280.0161.0000.2620.0530.7770.095
зачет до исправления0.1610.1210.0170.0400.1020.0000.0000.0160.1610.0520.1040.0760.0040.1720.1360.1270.0000.3850.0210.0210.1000.3240.3210.2621.0000.0190.0290.029
удовлетворительно до исправления0.1180.0380.0800.0510.0870.0000.0000.0210.1100.0850.0810.0340.0000.0710.2790.0660.0000.0600.3750.0770.0550.0000.6360.0530.0191.0000.0000.019
хорошо до исправления0.0800.0640.0650.0330.0470.0000.0000.0000.0620.0240.0430.0450.0000.0000.0270.2980.0000.0230.0130.0650.0790.0230.0100.7770.0290.0001.0000.075
отчислен0.1100.1160.2500.0170.2070.0440.0620.3840.1720.2110.0910.1810.0780.1070.0260.1390.0900.0850.0260.2000.1760.0430.0240.0950.0290.0190.0751.000

Missing values

2023-10-02T19:03:05.709563image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-02T19:03:06.197680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

СеместрФорма обученияКвалификациянезачет сразузачет сразуудовлетворительно сразухорошо сразуотлично сразузачет с исправлениемудовлетворительно с исправлениемхорошо с исправлениемотлично с исправлениемнезачет до исправлениязачет до исправленияудовлетворительно до исправленияхорошо до исправленияНакоп незачет сразуНакоп зачет сразуНакоп удовлетворительно сразуНакоп хорошо сразуНакоп отлично сразуНакоп зачет с исправлениемНакоп удовлетворительно с исправлениемНакоп хорошо с исправлениемНакоп отлично с исправлениемНакоп незачет до исправленияНакоп зачет до исправленияНакоп удовлетворительно до исправленияотчислен
012006.00.01.01.00.00.00.00.00.00.00.00.00.06.00.01.01.00.00.00.00.00.00.00.00
122005.01.00.02.00.00.01.00.00.00.01.00.00.011.01.01.03.00.00.01.00.00.00.01.00
232002.00.02.01.00.00.01.00.00.00.01.00.00.013.01.03.04.00.00.02.00.00.00.02.00
342004.00.00.02.00.00.00.01.00.00.01.00.00.017.01.03.06.00.00.02.01.00.00.03.00
452005.00.02.00.00.00.01.00.00.00.01.00.00.022.01.05.06.00.00.03.01.00.00.04.00
562004.00.02.00.00.00.01.00.00.00.01.00.00.026.01.07.06.00.00.04.01.00.00.05.00
672003.00.03.00.00.00.01.00.00.00.01.00.00.029.01.010.06.00.00.05.01.00.00.06.00
782005.00.01.01.00.00.00.01.00.00.00.01.00.034.01.011.07.00.00.05.02.00.00.06.00
892003.00.02.00.00.00.00.00.00.00.00.00.00.037.01.013.07.00.00.05.02.00.00.06.00
9102001.00.00.01.00.00.00.01.00.00.00.01.00.038.01.013.08.00.00.05.03.00.00.06.00
СеместрФорма обученияКвалификациянезачет сразузачет сразуудовлетворительно сразухорошо сразуотлично сразузачет с исправлениемудовлетворительно с исправлениемхорошо с исправлениемотлично с исправлениемнезачет до исправлениязачет до исправленияудовлетворительно до исправленияхорошо до исправленияНакоп незачет сразуНакоп зачет сразуНакоп удовлетворительно сразуНакоп хорошо сразуНакоп отлично сразуНакоп зачет с исправлениемНакоп удовлетворительно с исправлениемНакоп хорошо с исправлениемНакоп отлично с исправлениемНакоп незачет до исправленияНакоп зачет до исправленияНакоп удовлетворительно до исправленияотчислен
2015522005.01.03.00.00.00.00.00.00.00.00.00.00.012.03.010.00.00.00.00.00.00.00.00.01
2015622005.02.01.00.00.00.00.00.00.00.00.00.00.017.05.011.00.00.00.00.00.00.00.00.01
2015732003.02.01.00.00.00.00.00.00.00.00.00.08.025.08.014.00.00.00.00.00.00.00.00.01
2015842003.01.03.00.00.00.00.00.00.00.00.00.08.028.09.017.00.00.00.00.00.00.00.00.01
2015952006.00.01.01.00.00.00.00.00.00.00.00.08.034.09.018.01.00.00.00.00.00.00.00.01
2016062006.00.02.00.00.00.00.00.00.00.00.00.08.040.09.020.01.00.00.00.00.00.00.00.01
2016172004.00.02.01.00.00.00.00.00.00.00.00.08.044.09.022.02.00.00.00.00.00.00.00.01
2016282005.00.01.01.00.00.00.01.00.00.00.01.08.049.09.023.03.00.00.00.01.00.00.00.01
2016392002.01.02.00.00.00.00.01.00.00.00.01.08.051.010.025.03.00.00.00.02.00.00.00.01
20164102001.00.02.00.00.00.00.00.00.00.00.00.08.052.010.027.03.00.00.00.02.00.00.00.01

Duplicate rows

Most frequently occurring

СеместрФорма обученияКвалификациянезачет сразузачет сразуудовлетворительно сразухорошо сразуотлично сразузачет с исправлениемудовлетворительно с исправлениемхорошо с исправлениемотлично с исправлениемнезачет до исправлениязачет до исправленияудовлетворительно до исправленияхорошо до исправленияНакоп незачет сразуНакоп зачет сразуНакоп удовлетворительно сразуНакоп хорошо сразуНакоп отлично сразуНакоп зачет с исправлениемНакоп удовлетворительно с исправлениемНакоп хорошо с исправлениемНакоп отлично с исправлениемНакоп незачет до исправленияНакоп зачет до исправленияНакоп удовлетворительно до исправленияотчислен# duplicates
16510204.00.00.03.00.00.00.00.00.00.00.00.00.04.00.00.03.00.00.00.00.00.00.00.00232
21410302.00.00.00.00.00.00.00.00.00.00.00.00.02.00.00.00.00.00.00.00.00.00.00.00171
56620204.00.00.03.00.00.00.00.00.00.00.00.00.08.00.00.06.00.00.00.00.00.00.00.00128
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